The COP27 is currently taking place in Sharm El-Sheikh, Egypt. How can AI help?

António Guterres, a previous secretary-general at the UN, spoke about an AI-backed tool for tracking greenhouse gas emissions. Climate TRACE combines satellite data and artificial intelligence to show the facility-level emissions of over 70,000 sites around the world. This will allow leaders to know the location and scope of emissions being released. 📍

Google also published a blog post explaining how they are investing in technologies that can help communities prepare for and respond to climate-related disasters and threats. 

They developed FloodHub to show where floods are happening and where they are forecasted to happen thanks to their AI models. Other projects expand to using ML on satellite data to map wildfires and identifying areas that need help after hurricanes. Their technology was deployed in partnership with GiveDirectly  to quickly allocate aid to those most affected. 🆘

You have likely been faced with the limits of CPUs when training ML models, processing large datasets and playing around with AI. ⌛️

IBM Research has unveiled plans to build a prototype chip specialized for artificial intelligence, named the Artificial Intelligence Unit (AIU).

CPUs are well suited for general-purpose software applications. However, this also comes at a disadvantage when it comes to training and running deep learning models, which require massively parallel AI operations. ➕

The AIU will be adapted to perform calculations typically present in deep learning models more efficiently. Another novelty is in how it streamlines AI workflows by sending data directly from one compute engine to the next. Ultimately, this chip will allow to deploy AI at industrial scale. ↗️ 

Marine litter is an unsolved and growing issue that will only be exacerbated by climate change and extreme weather events, as they will contribute to transporting waste into the sea.

Previously, the detection of floating plastic was done with remote sensing, by analyzing the spectral response in images and creating indices such as the Floating Debris Index (FDI) or the Plastic Index (PI). The use of such methods was limited by the resolution of the images and often spectra were mixed with a background water signal. 🌊

By implementing a U-Net, the researchers hoped to rely on spatial rather than spectral features and propose a scalable waste detection method. ♻️ Using hand-labeled images from the work done using spectral methods, they trained their model to classify pixels. They also include atmospheric noise and other floating objects in their data.

They have made their dataset open-source to encourage work, which you can find here. Original paper here.

Due to the increasing interest in the impact of sleep on health, Google announced the integration of enhanced sleep sensing in its Nest Hub. 😴 Going beyond sleep schedule and duration tracking, they want to provide deeper insights by analysing disturbance events. ⚠️

By training a model with transfer learning techniques, Sleep Sensing predicts sleep stages and disambiguates the source of sleep disturbances from radar and microphone signals. 🔊

Google updated their algorithms such as to separate the different sources of noise in the room. Snoring will correspond closely with inhalations and exhalations, while other noises might vary independently.  Google also addresses privacy concerns by using on-device audio processing, with no raw audio data sent to Google’s servers.

Finally, Sleep Sensing could also open up opportunities in detecting respiratory diseases, such as cystic fibrosis, a rare disease. By quantifying nighttime cough, treatment effectiveness and response could be monitored. 💊

I've stopped using box plots. Should you?

Controversial idea: box plots are not an intuitive visualization tool ❌ This is the statement that Nick Desbarats shares in his article for the Data Visualization Society.

Nick believes in a more human-friendly way to display distributions, such as for example strip plots or jitter plots.

Why? First, he stated that box plots are straight up not visually intuitive 🤯 If it requires users to have explanations or certain a priori statistical knowledge, then it might not be a good tool to communicate results with a general public, or decision-makers. 

Humans interpret bigger lengths as bigger quantities, but longer whiskers in a box plot have the exact opposite meaning. Whatever the length of a whisker, it contains the same amount of data, which can be counter-intuitive.

Finally, he reminds us that they conceal information. Two similar box plots could have very different underlying distributions.

So, will you be exploring other chart types from now on? 📉

How good is a landscape? At first glance, an answer to such a question seems to only be a matter of taste. 

However, researchers from the ECEO lab at EPFL and the Wageningen University in the Netherlands developed deep learning-based models of landscape scenicness. 🌄

Using Flickr images of Great Britain landscapes, they used two neural networks to respectively predict scene categories (e.g., rainforest, lagoon, gardens…) and scenicness (i.e., aesthetic quality). 

These models yielded comparable accuracy to traditional models, which only tend to rely on environmental indicators and ignore public interactions with the landscape. Integrating this component should help build more comprehensive ecosystem preservation policies. ✍️

The original paper, published in Nature, can be found here. For people in a hurry 🏃, a summary is available here.